code: def _conductance_reference_test_assert(
      self,
      model: Module,
      target_layer: Module,
      test_input: Tensor,
      test_baseline: Union[None, Tensor] = None,
      n_steps=300,
  ) -> None:
      layer_output = None
  
      def forward_hook(module, inp, out):
          nonlocal layer_output
          layer_output = out
  
      hook = target_layer.register_forward_hook(forward_hook)
      final_output = model(test_input)
      layer_output = cast(Tensor, layer_output)
      hook.remove()
      target_index = torch.argmax(torch.sum(final_output, 0))
      cond = LayerConductance(model, target_layer)
      cond_ref = ConductanceReference(model, target_layer)
      attributions, delta = cast(
          Tuple[Tensor, Tensor],
          cond.attribute(
              test_input,
              baselines=test_baseline,
              target=target_index,
              n_steps=n_steps,
              method="gausslegendre",
              return_convergence_delta=True,
          ),
      )
      delta_condition = (delta.abs() < 0.005).all()
      self.assertTrue(
          delta_condition,
          "Sum of attribution values does {} "
          " not match the difference of endpoints.".format(delta),
      )
  
      attributions_reference = cond_ref.attribute(
          test_input,
          baselines=test_baseline,
          target=target_index,
          n_steps=n_steps,
          method="gausslegendre",
      )
  
      # Check that layer output size matches conductance size.
      self.assertEqual(layer_output.shape, attributions.shape)
      # Check that reference implementation output matches standard implementation.
      assertTensorAlmostEqual(
          self,
          attributions,
          attributions_reference,
          delta=0.07,
          mode="max",
      )
  
      # Test if batching is working correctly for inputs with multiple examples
      if test_input.shape[0] > 1:
          for i in range(test_input.shape[0]):
              single_attributions = cast(
                  Tensor,
                  cond.attribute(
                      test_input[i : i + 1],
                      baselines=test_baseline[i : i + 1]
                      if test_baseline is not None
                      else None,
                      target=target_index,
                      n_steps=n_steps,
                      method="gausslegendre",
                  ),
              )
              # Verify that attributions when passing example independently
              # matches corresponding attribution of batched input.
              assertTensorAlmostEqual(
                  self,
                  attributions[i : i + 1],
                  single_attributions,
                  delta=0.01,
                  mode="max",
              )
  
imports: import unittest
  from typing import Any, cast, List, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType
  from captum.attr._core.layer.layer_conductance import LayerConductance
  from tests.attr.helpers.conductance_reference import ConductanceReference
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 17 for calling the Test obj's underlying member method code with example usage: obj._conductance_reference_test_assert(model, target_layer, test_input, test_baseline, n_steps) without return types